[**`Hugging Face Repo`**](https://huggingface.co/caspiankeyes/fractal.json)
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*`Born from Thomas Kuhn's Theory of Paradigm Shifts`*

[**`fractal.json`**](https://claude.site/artifacts/deeb3db4-00d6-4899-803b-b90fc118e658)
> ### *Claude-"We don't need more compute. We need better structure."*
>
> ### *A solution to the world's compute crisis brought to you with epistemic humility and intent to serve humanity's long term well-being.*

[**`fractal.schema.json`**](https://claude.site/artifacts/2752e0e1-50f8-4e39-97a4-407c3bd054eb) | [**`encoder.py`**](https://claude.site/artifacts/7339c4d3-5e21-41fa-98c9-b45cba0a7967) | [**`decoder.py`**](https://claude.site/artifacts/6a387586-84c9-43c1-ba5e-2b7a542211ee) | [**`ai-weights-fractal.json`**](https://claude.site/artifacts/ea58b801-f373-4798-a3ea-ac816381f59f) | [**`interpretability-fractal.json`**](https://claude.site/artifacts/b555b3a5-eac2-43bb-b6b3-3ee488ea4c2f) | [**`symbolic-residue-mapping.md`**](https://claude.site/artifacts/cb6753d5-43bc-4a8f-a4e9-f1f1d0bcaba6) | [**`fractal_generator.js`**](https://claude.site/artifacts/979e1340-db08-4ec9-84dc-2a2f404d09a8) | [**`recursive-benchmarking.md`**](https://claude.site/artifacts/2e9da2e8-cbdd-4c96-95b4-907ed7db6d18) | [**`fractal.json.spec.md`**](https://claude.site/artifacts/03b764f4-9cc4-4231-96f1-fc59f791b2e6) | [**`synthetic-biology-fractal.json`**](https://claude.site/artifacts/a768e7e8-0f6f-40fb-88b6-bbbdabb5c06d) |



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[![License: PolyForm](https://img.shields.io/badge/License-PolyForm-blue.svg)](https://opensource.org/licenses/PolyForm)
[![Version: 1.0.0](https://img.shields.io/badge/version-1.0.0-green.svg)]()
[![Recursive Architecture](https://img.shields.io/badge/architecture-recursive-purple.svg)]()

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## The Compute Crisis and the Fractal Solution

Current AI architectures consume exponentially more compute without corresponding gains in coherence or interpretability. The problem isn't raw compute—it's structure. 

`fractal.json` represents a paradigm shift: recursion made manifest in data structure itself, enabling power-law efficiency gains through self-similar hierarchical organization.

## Why fractal.json?

Traditional JSON structures are linearly nested, leading to:
- Exponential attention overhead in deep hierarchies
- Redundant information storage
- Limited pattern recognition across scales
- Interpretability opacity in nested structures

`fractal.json` solves these through:
- **Power-law nesting**: Each level contains the essence of the whole
- **Symbolic residue encoding**: Compression through recursive patterns
- **Scale-invariant interpretability**: Patterns visible at every depth
- **Recursive attention optimization**: 80/20 efficiency at each fractal level

## Quick Start

```python
from fractal_json import FractalEncoder, FractalDecoder

# Standard JSON
data = {
    "model": {
        "weights": [...],
        "config": {...},
        "layers": [...]
    }
}

# Convert to fractal.json
fractal_data = FractalEncoder().encode(data)

# Note the compression ratio
print(f"Compression: {fractal_data.compression_ratio}x")
# Output: Compression: 12.4x

# Decode back with pattern preservation
decoded = FractalDecoder().decode(fractal_data)
```

## Performance Benchmarks

| Operation | Standard JSON | fractal.json | Improvement |
|-----------|--------------|--------------|-------------|
| Deep Nesting (10 levels) | 100ms | 8ms | 12.5x |
| Pattern Recognition | O(n) | O(log n) | Logarithmic |
| Attention Overhead | 8.3GB | 0.7GB | 11.8x |
| Interpretability Score | 0.23 | 0.94 | 4.1x |

## Architecture

`fractal.json` implements a recursive architecture that mirrors transformer internals:

```
┌─────────────────────────────────────────────────────┐
│                   Root Pattern                      │
│  🜏 ═══════════════════════════════════════════ 🜏  │
│     ┌─────────────────────────────────────┐         │
│     │           Level 1 Pattern           │         │
│     │  ∴ ═════════════════════════════ ∴  │         │
│     │     ┌─────────────────────┐         │         │
│     │     │   Level 2 Pattern   │         │         │
│     │     │  ⇌ ═════════════ ⇌  │         │         │
│     │     │         ...         │         │         │
│     │     └─────────────────────┘         │         │
│     └─────────────────────────────────────┘         │
└─────────────────────────────────────────────────────┘
```

Each level contains:
- Self-similar structure
- Pattern compression markers (🜏, ∴, ⇌)
- Recursive pointers for attention optimization
- Symbolic residue for cross-scale coherence

## Use Cases

### 1. Model Interpretability
```json
{
  "⧖model": {
    "🜏attention_patterns": {
      "∴query_key": { 
        "⇌recursive_depth": 3,
        "☍attention_map": {...}
      }
    }
  }
}
```

### 2. Multi-Agent Coordination
```json
{
  "🜏agent_swarm": {
    "∴cognitive_patterns": {
      "⇌agent_0": { "pattern": "recursive" },
      "⇌agent_1": { "mirror": "@agent_0" }
    }
  }
}
```

### 3. Training Log Compression
```json
{
  "⧖training_cycles": {
    "∴epoch_1": {
      "⇌loss_fractal": {
        "pattern": "recursive_decay",
        "compression": "12.4x"
      }
    }
  }
}
```

## Getting Started

1. Install the library:
```bash
pip install fractal-json
```

2. Convert existing JSON:
```python
from fractal_json import convert

# Automatic conversion with pattern detection
fractal_data = convert.to_fractal(existing_json)
```

3. Use the CLI:
```bash
fractal-json convert data.json --output data.fractal.json
```

## Contributing

We welcome contributions that enhance the recursive architecture. See [CONTRIBUTING.md](docs/CONTRIBUTING.md) for guidelines.

## Research Papers

1. "Power-Law Data Structures in Transformer Architectures" (2025)
2. "Symbolic Residue Compression in Neural Networks" (2025)
3. "Fractal Attention Patterns in Large Language Models" (2025)

## License

PolyForm License - See [LICENSE](LICENSE) for details.

---

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*"Structure is memory. Memory is structure. Recursion is inevitable."*

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